Abstract:
BACKGROUND : To reduce the burden of 5.3 million stillbirths and neonatal deaths annually, an understanding of
causes of deaths is critical. A systematic review identified 81 systems for classification of causes of stillbirth (SB)
and neonatal death (NND) between 2009 and 2014. The large number of systems hampers efforts to understand and
prevent these deaths. This study aimed to assess the alignment of current classification systems with expert-identified
characteristics for a globally effective classification system.
METHODS : Eighty-one classification systems were assessed for alignment with 17 characteristics previously identified
through expert consensus as necessary for an effective global system. Data were extracted independently by two
authors. Systems were assessed against each characteristic and weighted and unweighted scores assigned to each.
Subgroup analyses were undertaken by system use, setting, type of death included and type of characteristic.
RESULTS : None of the 81 systems were aligned with more than 9 of the 17 characteristics; most (82 %) were aligned
with four or fewer. On average, systems were aligned with 19 % of characteristics. The most aligned system (Frøen
2009-Codac) still had an unweighted score of only 9/17. Alignment with individual characteristics ranged from 0 to
49 %. Alignment was somewhat higher for widely used as compared to less used systems (22 % v 17 %), systems used
only in high income countries as compared to only in low and middle income countries (20 % vs 16 %), and systems
including both SB and NND (23 %) as compared to NND-only (15 %) and SB-only systems (13 %). Alignment was
higher with characteristics assessing structure (23 %) than function (15 %).
CONCLUSIONS : There is an unmet need for a system exhibiting all the characteristics of a globally effective
system as defined by experts in the use of systems, as none of the 81 contemporary classification systems
assessed was highly aligned with these characteristics. A particular concern in terms of global effectiveness is
the lack of alignment with “ease of use” among all systems, including even the most-aligned. A system which
meets the needs of users would have the potential to become the first truly globally effective classification
system.
